From Packets to People: Quality of Experience as a New Measurement Challenge

Schatz, Raimund; Hoßfeld, Tobias; Janowski, Lucjan; Egger, Sebastian

doi:10.1007/978-3-642-36784-7_10

Cited by 88 publications

(62 citation statements)

References 80 publications

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“…Progress of performance in VoIP. in one-way or another and they have been employed successfully in many fields [13] [14]. One of the most successful AI branches is Artificial Neural Networks (ANNs).…”

Section: The Proposed Solutionmentioning

confidence: 99%

Voice Quality for Internet Protocol Based on Neural Network Model

Al-Wahshat¹,

Al‐Maitah²,

Smadi³

2017

JSIP

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The constant growth of the number of Internet users places new demands on the bandwidth of modern communication. The World Wide Web has led to the emergence of various types of traffic, Graphics information, voice data, as well as various video applications demanding their special requirements for such networks. To meet all the requests, one increase of the network is not enough. As the number of Internet users and the various networks annex offices increases with each passing day, network needs a means of control that would ensure the support of both existing and new applications and services. There is still a need to address a number of issues before it announces the VoIP as the ultimate replacement of the traditional telephone network. This work provides an overview of the major benefits of VoIP network, as well as the presentation of a new way around the problem of ensuring the high quality of service for VoIP protocol on the basis of neural network model.

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Section: The Proposed Solutionmentioning

confidence: 99%

Voice Quality for Internet Protocol Based on Neural Network Model

Al-Wahshat¹,

Al‐Maitah²,

Smadi³

2017

JSIP

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“…Schatz et al give comprehensive insights in [29]. They define two different kinds of testing techniques for QoE: The first kind is made of subjective tests, typically conducted in a controlled laboratory setting but also as field tests or using crowdsourcing methods.…”

Section: Problem Statementmentioning

confidence: 99%

Towards Internet Scale Quality-of-Experience Measurement with Twitter

Kergl

Roedler

Rodosek

2017

Lecture Notes in Computer Science

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Abstract. At present, Quality of Experience (QoE) measurements are accomplished by interrogating users for the perceived quality of a service they just have used. Influenced by many factors and often limited by domain or geographical region, this technique has several drawbacks when a general state of QoE for the internet as a whole is prospected. To achieve such a general metric, we leverage user complaints that we observe in real-time in social media. Such approaches have been successfully applied for the monitoring of specific and single services. We aim to extend existing methods in order to create an overall metric, define an internet wide QoE baseline, monitor changes and hence, provide a context for assessing smaller scale findings against a ground truth. The contribution of this work is to demonstrate the feasibility of using social media analysis for generating a meaningful value for quantifying the actual QoE of the internet.

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“…R. Schatz et al [48] recommended Absolute Category Rating with Hidden Reference (ACR-HR) test method with five-point quality scale as the mean value and the confidence intervals of the results from different methods do not deviate much. However, the averaged value of the user ratings gathered in five-level ACR scale has a set of drawbacks such as the weakness in the definition of quality as summarized in [20,48]. In addition, it is influenced by the "forgiveness effect" [53].…”

Section: Inferring Qoe Based On Inter-picture Timementioning

confidence: 99%

“…However, due to the inconsistency in the definition of experiment settings at different standards documents (ITU-R BT.500, ITU-T P.910/1/2, ITU-R BT.1438), this is still a challenge [48]. One of the strengths of our study is that we run the subjective tests in peoples' natural daily life settings.…”

Section: User Study (Part 2)mentioning

confidence: 99%

VLQoE: Video QoE instrumentation on the smartphone

Ickin

Fiedler

Wac

et al. 2014

Multimed Tools Appl

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The usage of network-demanding applications is growing rapidly such as video streaming on mobile terminals. However, network and/or service providers might not guarantee the perceived quality for video streaming that demands high packet transmission rate. In order to satisfy the user expectations and to minimize user churn, it is important for network operators to infer the end-user perceived quality in video streaming. Today, the most reliable method to obtain end-user perceived quality is through subjective tests, and the preferred location is the user interface as it is the closest point of application to the enduser. The end-user perceived quality on video streaming is highly influenced by occasional freezes; technically the extraordinary time gaps between two consecutive pictures that are displayed to the user, i.e., high inter-picture time. In this paper, we present a QoE instrumentation for video streaming, VLQoE. We added functionality to the VLC player to record Multimed Tools Appl a set of metrics from the user interface, application-level, network-level, and from the available sensors of the device. To the best of our knowledge, VLQoE is the first tool of its kind that can be used in user experiments for video streaming. By using the tool, we present a two state model based on the inter-picture time, for the HTTP-and RTSP-based video streaming via 3.5G. Next, we studied the influence of inter-picture time on the user perceived quality through out a user study. We investigated the minimum user perceived inter-picture time, and the user response time.

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From Packets to People: Quality of Experience as a New Measurement Challenge

Cited by 88 publications

References 80 publications

Voice Quality for Internet Protocol Based on Neural Network Model

Voice Quality for Internet Protocol Based on Neural Network Model

Towards Internet Scale Quality-of-Experience Measurement with Twitter

VLQoE: Video QoE instrumentation on the smartphone

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